We are building a focused x-ray luminescence tomography (FXLT) imaging system, developing a machinelearning based FXLT reconstruction algorithm, and synthesizing nanophosphors with different emission wavelengths. In this paper, we will report our current progress from these three aspects. Briefly, we mount all main components, including the focused x-ray tube, the fiber detector, and the x-ray tube and x-ray detector for a microCT system, on a rotary which is a heavy-duty ring track. A microCT scan will be performed before FXLT scan. For a FXLT scan, we will have four PMTs to measure four fiber detectors at two different wavelengths simultaneously for each linear scan position. We expect the spatial resolution of the FXLT imaging will be around 100 micrometers and a limit of detection of approximately 2 μg/mL (for Gd2O2S:Eu).
X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality which has the potential for achieving both high sensitivity and spatial resolution simultaneously. For the narrow x-ray beam-based XLCT imaging, based on previous work, a spatial resolution of about double the x-ray beam size can be achieved using a translate/rotate scanning scheme, taking step sizes equal to the x-ray beam width. To break the current spatial resolution limit, we propose a scanning strategy achieved by reducing the scanning step size to be smaller than the x-ray beam size. We performed four sets of numerical simulations and a phantom experiment using cylindrical phantoms and have demonstrated that our proposed scanning method can greatly improve the XLCT-reconstructed image quality compared with the traditional scanning approach. In our simulations, by using a fixed x-ray beam size of 0.8 mm, we were able to successfully reconstruct six embedded targets as small as 0.5 mm in diameter and with the same edge-to-edge distances by using a scanning step as small as 0.2 mm which is a 1.6 times improvement in the spatial resolution compared with the traditional approach. Lastly, the phantom experiment further demonstrated the efficacy of our proposed method in improving the XLCT image quality, with all image quality metrics improving as the step size decreased.
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